expoFitter is a Shiny-based application designed for the analysis and modeling of electrophysiological data, with a focus on current vs. time traces commonly encountered in neuroscience research. The app provides tools for data visualization, preprocessing, and fitting of both single- and double-exponential models. This facilitates the study of dynamic processes in neuronal systems, such as ionic currents, calcium dynamics, or neural network behavior.
The application includes backend functions for filtering data, computing coefficients of determination (R²), and fitting models using non-linear least squares optimization. Additionally, the app provides an intuitive, interactive interface for setting model parameters and visualizing fitted models.
nlsLM
algorithm.flexdashboard for flexible, customizable data
exploration.To run the expoFitter application locally, ensure that R
is installed on your system along with the following packages:
dplyrminpack.lmDTshinyClone this repository to your local machine:
bash git clone https://github.com/your-username/expoFitter.git
Navigate to the repository directory:
bash cd expoFitter
Open R or RStudio and install the required packages:
r install.packages(c('dplyr', 'minpack.lm', 'DT', 'shiny'))
Run the application:
r shiny::runApp('path/to/repository')
Alternatively, you can deploy the app directly from RStudio using the
Run App button.
.csv file containing time and current data.
Ensure that the file has at least two columns:
A, B,
C, etc.)..pdf file by clicking the ‘Save’ button in the
interface.If you would like to contribute to expoFitter, please
adhere to the following guidelines:
Fork the Repository: Start by forking this repository to your own GitHub account.
Clone Your Fork: Clone the forked repository to
your local development environment.
bash git clone https://github.com/your-username/expoFitter.git
Create a New Branch: Make your changes in a new
branch. bash git checkout -b feature-name
Testing: Ensure that all new features are fully tested. Utilize mock datasets for validation of model fitting functionalities.
Submit a Pull Request: Once your changes are ready, submit a pull request with a clear description of your additions or modifications.
This project is licensed under the MIT License - see the LICENSE file for details.
This application is inspired by Dr. Ellison’s research in computational neuroscience, focusing on the analysis of electrophysiological processes in motor control and neural activity. The design leverages Dr. Ellison’s expertise in both the computational and experimental realms of neuroscience.
For further questions or collaboration, please contact:
Ryan Ellison, PhD
Email | GitHub
For more details on the scientific basis and applications of the expoFitter tool, please refer to Dr. Ellison’s publications and research in computational neuroscience and electrophysiological data analysis.